Object detection is related to computer vision and image processing applications that deals with detecting instances of semantic objects of a certain class (such as humans, buildings or cars) in digital images and videos. Object detection has applications in many areas of computer vision, including image retrieval, and video surveillance. However, conventional object detection methods have a number of problems.
For example, a major shortcoming of most conventional surveillance systems is a reliance on visible light cameras. Most methods for detecting objects work on visible light images or video and do not work at night without providing artificial light sources. However, such solutions can be expensive, and are not applicable for some surveillance applications.
Also, conventional methods for detecting objects in images include a scanning window approach. In those methods, a classifier “scans” every rectangular patch of a fixed, known size in the image. The classifier takes an image patch as an input image and outputs a binary result depending on whether the image patch includes the object, or not. To detect objects at larger scales, the input image is scaled-down to a smaller image and the classifier is scanned over the scaled-down image. The scaling is done repeatedly until the resized image is smaller than a size of the patch. However the scanning window approach is computationally complex.
Accordingly, there is a need in the art to address above problems.